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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

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Showing 3821–3830 of 9051 papers

TitleStatusHype
Improving the Transferability of Adversarial Examples by Feature Augmentation—0
From Lifestyle Vlogs to Everyday Interactions—0
Diverse, not Short: A Length-Controlled Self-Learning Framework for Improving Response Diversity of Language Models—0
Diverse Neural Network Learns True Target Functions—0
From Macro to Micro: Probing Dataset Diversity in Language Model Fine-Tuning—0
From Matching with Diversity Constraints to Matching with Regional Quotas—0
Boosting Dialog Response Generation—0
DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery—0
From Posterior Sampling to Meaningful Diversity in Image Restoration—0
DiverseMotion: Towards Diverse Human Motion Generation via Discrete Diffusion—0
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